Intrinsic Representation of Hyperspectral Imagery for Unsupervised Feature Extraction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unsupervised feature extraction from hyperspectral images (HSIs) relies on efficient data representation. However, classical data representation techniques, e.g., principal component analysis and independent component analysis, do not reflect the intrinsic characteristics of HSI, and as such, they are less efficient for producing discriminative features. To address this issue, we have developed an intrinsic representation (IR) approach to support HSI classification. Based on the linear spectral mixture model, the IR approach explains the underlying physical factors that are responsible for generating HSI. Moreover, it addresses other important characteristics of HSI, i.e., the noise variance heterogeneity effect in the spectral domain and the spatial correlation effect in image domain. The IR model is solved iteratively by alternating the estimation of IR coefficients given IR bases and the update of IR bases given the coefficients. The resulting IR coefficients are discriminative, compact, and noise resistant, thereby constituting powerful features for improved HSI classification. The experiments on both simulated and real HSI demonstrate that the features extracted by the IR model are more capable of boosting the classification performance than the other referenced techniques.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it